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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 19:56:37 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290542114jo5fk6bgjylfcoe.htm/, Retrieved Fri, 29 Mar 2024 13:40:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99624, Retrieved Fri, 29 Mar 2024 13:40:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [ws 7] [2010-11-23 19:56:37] [09489ba95453d3f5c9e6f2eaeda915af] [Current]
-   PD      [Multiple Regression] [WS 7 - minitutorial] [2010-11-24 18:11:33] [bd591a1ebb67d263a02e7adae3fa1a4d]
-   PD        [Multiple Regression] [meervoudige regre...] [2010-12-17 13:03:18] [bd591a1ebb67d263a02e7adae3fa1a4d]
-    D        [Multiple Regression] [multiple regressi...] [2010-12-17 16:58:50] [bd591a1ebb67d263a02e7adae3fa1a4d]
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Dataseries X:
14544.5	94.6
15116.3	95.9
17413.2	104.7
16181.5	102.8
15607.4	98.1
17160.9	113.9
14915.8	80.9
13768	95.7
17487.5	113.2
16198.1	105.9
17535.2	108.8
16571.8	102.3
16198.9	99
16554.2	100.7
19554.2	115.5
15903.8	100.7
18003.8	109.9
18329.6	114.6
16260.7	85.4
14851.9	100.5
18174.1	114.8
18406.6	116.5
18466.5	112.9
16016.5	102
17428.5	106
17167.2	105.3
19630	118.8
17183.6	106.1
18344.7	109.3
19301.4	117.2
18147.5	92.5
16192.9	104.2
18374.4	112.5
20515.2	122.4
18957.2	113.3
16471.5	100
18746.8	110.7
19009.5	112.8
19211.2	109.8
20547.7	117.3
19325.8	109.1
20605.5	115.9
20056.9	96
16141.4	99.8
20359.8	116.8
19711.6	115.7
15638.6	99.4
14384.5	94.3
13855.6	91
14308.3	93.2
15290.6	103.1
14423.8	94.1
13779.7	91.8
15686.3	102.7
14733.8	82.6
12522.5	89.1
16189.4	104.5
16059.1	105.1
16007.1	95.1
15806.8	88.7
15160	86.3
15692.1	91.8
18908.9	111.5
16969.9	99.7
16997.5	97.5
19858.9	111.7
17681.2	86.2
16006.9	95.4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99624&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99624&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99624&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
productie[t] = + 35.3845145648451 + 0.00398530347372131uitvoer[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
productie[t] =  +  35.3845145648451 +  0.00398530347372131uitvoer[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99624&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]productie[t] =  +  35.3845145648451 +  0.00398530347372131uitvoer[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99624&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99624&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
productie[t] = + 35.3845145648451 + 0.00398530347372131uitvoer[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)35.38451456484516.7820185.21742e-061e-06
uitvoer0.003985303473721310.00039610.05900

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 35.3845145648451 & 6.782018 & 5.2174 & 2e-06 & 1e-06 \tabularnewline
uitvoer & 0.00398530347372131 & 0.000396 & 10.059 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99624&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]35.3845145648451[/C][C]6.782018[/C][C]5.2174[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]uitvoer[/C][C]0.00398530347372131[/C][C]0.000396[/C][C]10.059[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99624&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99624&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)35.38451456484516.7820185.21742e-061e-06
uitvoer0.003985303473721310.00039610.05900







Multiple Linear Regression - Regression Statistics
Multiple R0.777960787487783
R-squared0.605222986868611
Adjusted R-squared0.599241516972681
F-TEST (value)101.182986355981
F-TEST (DF numerator)1
F-TEST (DF denominator)66
p-value5.99520433297585e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.29892155690996
Sum Squared Residuals2618.64324348693

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.777960787487783 \tabularnewline
R-squared & 0.605222986868611 \tabularnewline
Adjusted R-squared & 0.599241516972681 \tabularnewline
F-TEST (value) & 101.182986355981 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 66 \tabularnewline
p-value & 5.99520433297585e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.29892155690996 \tabularnewline
Sum Squared Residuals & 2618.64324348693 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99624&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.777960787487783[/C][/ROW]
[ROW][C]R-squared[/C][C]0.605222986868611[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.599241516972681[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]101.182986355981[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]66[/C][/ROW]
[ROW][C]p-value[/C][C]5.99520433297585e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.29892155690996[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2618.64324348693[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99624&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99624&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.777960787487783
R-squared0.605222986868611
Adjusted R-squared0.599241516972681
F-TEST (value)101.182986355981
F-TEST (DF numerator)1
F-TEST (DF denominator)66
p-value5.99520433297585e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.29892155690996
Sum Squared Residuals2618.64324348693







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
194.693.34876093838471.25123906161525
295.995.62755746465860.272442535341443
3104.7104.781401013449-0.0814010134490508
4102.899.87270272486652.92729727513349
598.197.58474000060310.515259999396888
6113.9103.77590894702910.1240910529708
780.994.8285041181774-13.9285041181774
895.790.25417279104015.44582720895987
9113.2105.0775090615478.12249093845346
10105.999.93885876253035.96114123746972
11108.8105.2676080372433.53239196275694
12102.3101.428166670660.87183332934006
139999.9420470053093-0.942047005309262
14100.7101.358025329522-0.658025329522445
15115.5113.3139357506862.18606424931362
16100.798.7659839502141.9340160497859
17109.9107.1351212450292.76487875497115
18114.6108.4335331167676.16646688323274
1985.4100.188338759985-14.7883387599852
20100.594.57384322620675.92615677379334
21114.8107.8138184266046.9861815733964
22116.5108.7404014842447.7595985157562
23112.9108.9791211623203.9208788376803
2410299.21512765170252.78487234829750
25106104.8423761565971.15762384340301
26105.3103.8010163589141.49898364108639
27118.8113.6160217539945.18397824600555
28106.1103.8663753358832.23362466411736
29109.3108.4937111992200.806288800779543
30117.2112.3064510325304.89354896747037
3192.5107.707809354203-15.2078093542026
32104.299.9181351844674.28186481553307
33112.5108.612074712393.88792528761002
34122.4117.1438123889335.25618761106745
35113.3110.9347095768752.36529042312524
36100101.028440732246-1.02844073224569
37110.7110.0962017260040.603798273996215
38112.8111.1431409485501.65685905144962
39109.8111.9469766592-2.14697665919997
40117.3117.2733347518290.0266652481714963
41109.1112.403692437288-3.30369243728843
42115.9117.503685292610-1.60368529260959
4396115.317347806926-19.3173478069261
4499.899.71289205557030.0871079444297108
45116.8116.5244962291160.275503770883736
46115.7113.941222517451.75877748254990
4799.497.70908146898321.69091853101679
4894.392.71111238258931.58888761741068
499190.60328537533810.396714624661882
5093.292.40743225789180.792567742108253
51103.196.32219586012826.7778041398718
5294.192.86773480910661.23226519089343
5391.890.30080084168271.49919915831732
54102.797.89918044467974.80081955532029
5582.694.1031788859602-11.5031788859602
5689.185.29047731452023.80952268547976
57104.599.9041866223094.59581337769109
58105.199.3849015796835.71509842031697
5995.199.1776657990495-4.07766579904952
6088.798.3794095132631-9.67940951326313
6186.395.8017152264602-9.5017152264602
6291.897.9222952048273-6.12229520482731
63111.5110.7422194190940.75778058090598
6499.7103.014715983548-3.3147159835484
6597.5103.124710359423-5.6247103594231
66111.7114.528257719129-2.82825771912926
6786.2105.849462344406-19.6494623444064
6895.499.1768687383548-3.77686873835476

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 94.6 & 93.3487609383847 & 1.25123906161525 \tabularnewline
2 & 95.9 & 95.6275574646586 & 0.272442535341443 \tabularnewline
3 & 104.7 & 104.781401013449 & -0.0814010134490508 \tabularnewline
4 & 102.8 & 99.8727027248665 & 2.92729727513349 \tabularnewline
5 & 98.1 & 97.5847400006031 & 0.515259999396888 \tabularnewline
6 & 113.9 & 103.775908947029 & 10.1240910529708 \tabularnewline
7 & 80.9 & 94.8285041181774 & -13.9285041181774 \tabularnewline
8 & 95.7 & 90.2541727910401 & 5.44582720895987 \tabularnewline
9 & 113.2 & 105.077509061547 & 8.12249093845346 \tabularnewline
10 & 105.9 & 99.9388587625303 & 5.96114123746972 \tabularnewline
11 & 108.8 & 105.267608037243 & 3.53239196275694 \tabularnewline
12 & 102.3 & 101.42816667066 & 0.87183332934006 \tabularnewline
13 & 99 & 99.9420470053093 & -0.942047005309262 \tabularnewline
14 & 100.7 & 101.358025329522 & -0.658025329522445 \tabularnewline
15 & 115.5 & 113.313935750686 & 2.18606424931362 \tabularnewline
16 & 100.7 & 98.765983950214 & 1.9340160497859 \tabularnewline
17 & 109.9 & 107.135121245029 & 2.76487875497115 \tabularnewline
18 & 114.6 & 108.433533116767 & 6.16646688323274 \tabularnewline
19 & 85.4 & 100.188338759985 & -14.7883387599852 \tabularnewline
20 & 100.5 & 94.5738432262067 & 5.92615677379334 \tabularnewline
21 & 114.8 & 107.813818426604 & 6.9861815733964 \tabularnewline
22 & 116.5 & 108.740401484244 & 7.7595985157562 \tabularnewline
23 & 112.9 & 108.979121162320 & 3.9208788376803 \tabularnewline
24 & 102 & 99.2151276517025 & 2.78487234829750 \tabularnewline
25 & 106 & 104.842376156597 & 1.15762384340301 \tabularnewline
26 & 105.3 & 103.801016358914 & 1.49898364108639 \tabularnewline
27 & 118.8 & 113.616021753994 & 5.18397824600555 \tabularnewline
28 & 106.1 & 103.866375335883 & 2.23362466411736 \tabularnewline
29 & 109.3 & 108.493711199220 & 0.806288800779543 \tabularnewline
30 & 117.2 & 112.306451032530 & 4.89354896747037 \tabularnewline
31 & 92.5 & 107.707809354203 & -15.2078093542026 \tabularnewline
32 & 104.2 & 99.918135184467 & 4.28186481553307 \tabularnewline
33 & 112.5 & 108.61207471239 & 3.88792528761002 \tabularnewline
34 & 122.4 & 117.143812388933 & 5.25618761106745 \tabularnewline
35 & 113.3 & 110.934709576875 & 2.36529042312524 \tabularnewline
36 & 100 & 101.028440732246 & -1.02844073224569 \tabularnewline
37 & 110.7 & 110.096201726004 & 0.603798273996215 \tabularnewline
38 & 112.8 & 111.143140948550 & 1.65685905144962 \tabularnewline
39 & 109.8 & 111.9469766592 & -2.14697665919997 \tabularnewline
40 & 117.3 & 117.273334751829 & 0.0266652481714963 \tabularnewline
41 & 109.1 & 112.403692437288 & -3.30369243728843 \tabularnewline
42 & 115.9 & 117.503685292610 & -1.60368529260959 \tabularnewline
43 & 96 & 115.317347806926 & -19.3173478069261 \tabularnewline
44 & 99.8 & 99.7128920555703 & 0.0871079444297108 \tabularnewline
45 & 116.8 & 116.524496229116 & 0.275503770883736 \tabularnewline
46 & 115.7 & 113.94122251745 & 1.75877748254990 \tabularnewline
47 & 99.4 & 97.7090814689832 & 1.69091853101679 \tabularnewline
48 & 94.3 & 92.7111123825893 & 1.58888761741068 \tabularnewline
49 & 91 & 90.6032853753381 & 0.396714624661882 \tabularnewline
50 & 93.2 & 92.4074322578918 & 0.792567742108253 \tabularnewline
51 & 103.1 & 96.3221958601282 & 6.7778041398718 \tabularnewline
52 & 94.1 & 92.8677348091066 & 1.23226519089343 \tabularnewline
53 & 91.8 & 90.3008008416827 & 1.49919915831732 \tabularnewline
54 & 102.7 & 97.8991804446797 & 4.80081955532029 \tabularnewline
55 & 82.6 & 94.1031788859602 & -11.5031788859602 \tabularnewline
56 & 89.1 & 85.2904773145202 & 3.80952268547976 \tabularnewline
57 & 104.5 & 99.904186622309 & 4.59581337769109 \tabularnewline
58 & 105.1 & 99.384901579683 & 5.71509842031697 \tabularnewline
59 & 95.1 & 99.1776657990495 & -4.07766579904952 \tabularnewline
60 & 88.7 & 98.3794095132631 & -9.67940951326313 \tabularnewline
61 & 86.3 & 95.8017152264602 & -9.5017152264602 \tabularnewline
62 & 91.8 & 97.9222952048273 & -6.12229520482731 \tabularnewline
63 & 111.5 & 110.742219419094 & 0.75778058090598 \tabularnewline
64 & 99.7 & 103.014715983548 & -3.3147159835484 \tabularnewline
65 & 97.5 & 103.124710359423 & -5.6247103594231 \tabularnewline
66 & 111.7 & 114.528257719129 & -2.82825771912926 \tabularnewline
67 & 86.2 & 105.849462344406 & -19.6494623444064 \tabularnewline
68 & 95.4 & 99.1768687383548 & -3.77686873835476 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99624&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]94.6[/C][C]93.3487609383847[/C][C]1.25123906161525[/C][/ROW]
[ROW][C]2[/C][C]95.9[/C][C]95.6275574646586[/C][C]0.272442535341443[/C][/ROW]
[ROW][C]3[/C][C]104.7[/C][C]104.781401013449[/C][C]-0.0814010134490508[/C][/ROW]
[ROW][C]4[/C][C]102.8[/C][C]99.8727027248665[/C][C]2.92729727513349[/C][/ROW]
[ROW][C]5[/C][C]98.1[/C][C]97.5847400006031[/C][C]0.515259999396888[/C][/ROW]
[ROW][C]6[/C][C]113.9[/C][C]103.775908947029[/C][C]10.1240910529708[/C][/ROW]
[ROW][C]7[/C][C]80.9[/C][C]94.8285041181774[/C][C]-13.9285041181774[/C][/ROW]
[ROW][C]8[/C][C]95.7[/C][C]90.2541727910401[/C][C]5.44582720895987[/C][/ROW]
[ROW][C]9[/C][C]113.2[/C][C]105.077509061547[/C][C]8.12249093845346[/C][/ROW]
[ROW][C]10[/C][C]105.9[/C][C]99.9388587625303[/C][C]5.96114123746972[/C][/ROW]
[ROW][C]11[/C][C]108.8[/C][C]105.267608037243[/C][C]3.53239196275694[/C][/ROW]
[ROW][C]12[/C][C]102.3[/C][C]101.42816667066[/C][C]0.87183332934006[/C][/ROW]
[ROW][C]13[/C][C]99[/C][C]99.9420470053093[/C][C]-0.942047005309262[/C][/ROW]
[ROW][C]14[/C][C]100.7[/C][C]101.358025329522[/C][C]-0.658025329522445[/C][/ROW]
[ROW][C]15[/C][C]115.5[/C][C]113.313935750686[/C][C]2.18606424931362[/C][/ROW]
[ROW][C]16[/C][C]100.7[/C][C]98.765983950214[/C][C]1.9340160497859[/C][/ROW]
[ROW][C]17[/C][C]109.9[/C][C]107.135121245029[/C][C]2.76487875497115[/C][/ROW]
[ROW][C]18[/C][C]114.6[/C][C]108.433533116767[/C][C]6.16646688323274[/C][/ROW]
[ROW][C]19[/C][C]85.4[/C][C]100.188338759985[/C][C]-14.7883387599852[/C][/ROW]
[ROW][C]20[/C][C]100.5[/C][C]94.5738432262067[/C][C]5.92615677379334[/C][/ROW]
[ROW][C]21[/C][C]114.8[/C][C]107.813818426604[/C][C]6.9861815733964[/C][/ROW]
[ROW][C]22[/C][C]116.5[/C][C]108.740401484244[/C][C]7.7595985157562[/C][/ROW]
[ROW][C]23[/C][C]112.9[/C][C]108.979121162320[/C][C]3.9208788376803[/C][/ROW]
[ROW][C]24[/C][C]102[/C][C]99.2151276517025[/C][C]2.78487234829750[/C][/ROW]
[ROW][C]25[/C][C]106[/C][C]104.842376156597[/C][C]1.15762384340301[/C][/ROW]
[ROW][C]26[/C][C]105.3[/C][C]103.801016358914[/C][C]1.49898364108639[/C][/ROW]
[ROW][C]27[/C][C]118.8[/C][C]113.616021753994[/C][C]5.18397824600555[/C][/ROW]
[ROW][C]28[/C][C]106.1[/C][C]103.866375335883[/C][C]2.23362466411736[/C][/ROW]
[ROW][C]29[/C][C]109.3[/C][C]108.493711199220[/C][C]0.806288800779543[/C][/ROW]
[ROW][C]30[/C][C]117.2[/C][C]112.306451032530[/C][C]4.89354896747037[/C][/ROW]
[ROW][C]31[/C][C]92.5[/C][C]107.707809354203[/C][C]-15.2078093542026[/C][/ROW]
[ROW][C]32[/C][C]104.2[/C][C]99.918135184467[/C][C]4.28186481553307[/C][/ROW]
[ROW][C]33[/C][C]112.5[/C][C]108.61207471239[/C][C]3.88792528761002[/C][/ROW]
[ROW][C]34[/C][C]122.4[/C][C]117.143812388933[/C][C]5.25618761106745[/C][/ROW]
[ROW][C]35[/C][C]113.3[/C][C]110.934709576875[/C][C]2.36529042312524[/C][/ROW]
[ROW][C]36[/C][C]100[/C][C]101.028440732246[/C][C]-1.02844073224569[/C][/ROW]
[ROW][C]37[/C][C]110.7[/C][C]110.096201726004[/C][C]0.603798273996215[/C][/ROW]
[ROW][C]38[/C][C]112.8[/C][C]111.143140948550[/C][C]1.65685905144962[/C][/ROW]
[ROW][C]39[/C][C]109.8[/C][C]111.9469766592[/C][C]-2.14697665919997[/C][/ROW]
[ROW][C]40[/C][C]117.3[/C][C]117.273334751829[/C][C]0.0266652481714963[/C][/ROW]
[ROW][C]41[/C][C]109.1[/C][C]112.403692437288[/C][C]-3.30369243728843[/C][/ROW]
[ROW][C]42[/C][C]115.9[/C][C]117.503685292610[/C][C]-1.60368529260959[/C][/ROW]
[ROW][C]43[/C][C]96[/C][C]115.317347806926[/C][C]-19.3173478069261[/C][/ROW]
[ROW][C]44[/C][C]99.8[/C][C]99.7128920555703[/C][C]0.0871079444297108[/C][/ROW]
[ROW][C]45[/C][C]116.8[/C][C]116.524496229116[/C][C]0.275503770883736[/C][/ROW]
[ROW][C]46[/C][C]115.7[/C][C]113.94122251745[/C][C]1.75877748254990[/C][/ROW]
[ROW][C]47[/C][C]99.4[/C][C]97.7090814689832[/C][C]1.69091853101679[/C][/ROW]
[ROW][C]48[/C][C]94.3[/C][C]92.7111123825893[/C][C]1.58888761741068[/C][/ROW]
[ROW][C]49[/C][C]91[/C][C]90.6032853753381[/C][C]0.396714624661882[/C][/ROW]
[ROW][C]50[/C][C]93.2[/C][C]92.4074322578918[/C][C]0.792567742108253[/C][/ROW]
[ROW][C]51[/C][C]103.1[/C][C]96.3221958601282[/C][C]6.7778041398718[/C][/ROW]
[ROW][C]52[/C][C]94.1[/C][C]92.8677348091066[/C][C]1.23226519089343[/C][/ROW]
[ROW][C]53[/C][C]91.8[/C][C]90.3008008416827[/C][C]1.49919915831732[/C][/ROW]
[ROW][C]54[/C][C]102.7[/C][C]97.8991804446797[/C][C]4.80081955532029[/C][/ROW]
[ROW][C]55[/C][C]82.6[/C][C]94.1031788859602[/C][C]-11.5031788859602[/C][/ROW]
[ROW][C]56[/C][C]89.1[/C][C]85.2904773145202[/C][C]3.80952268547976[/C][/ROW]
[ROW][C]57[/C][C]104.5[/C][C]99.904186622309[/C][C]4.59581337769109[/C][/ROW]
[ROW][C]58[/C][C]105.1[/C][C]99.384901579683[/C][C]5.71509842031697[/C][/ROW]
[ROW][C]59[/C][C]95.1[/C][C]99.1776657990495[/C][C]-4.07766579904952[/C][/ROW]
[ROW][C]60[/C][C]88.7[/C][C]98.3794095132631[/C][C]-9.67940951326313[/C][/ROW]
[ROW][C]61[/C][C]86.3[/C][C]95.8017152264602[/C][C]-9.5017152264602[/C][/ROW]
[ROW][C]62[/C][C]91.8[/C][C]97.9222952048273[/C][C]-6.12229520482731[/C][/ROW]
[ROW][C]63[/C][C]111.5[/C][C]110.742219419094[/C][C]0.75778058090598[/C][/ROW]
[ROW][C]64[/C][C]99.7[/C][C]103.014715983548[/C][C]-3.3147159835484[/C][/ROW]
[ROW][C]65[/C][C]97.5[/C][C]103.124710359423[/C][C]-5.6247103594231[/C][/ROW]
[ROW][C]66[/C][C]111.7[/C][C]114.528257719129[/C][C]-2.82825771912926[/C][/ROW]
[ROW][C]67[/C][C]86.2[/C][C]105.849462344406[/C][C]-19.6494623444064[/C][/ROW]
[ROW][C]68[/C][C]95.4[/C][C]99.1768687383548[/C][C]-3.77686873835476[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99624&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99624&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
194.693.34876093838471.25123906161525
295.995.62755746465860.272442535341443
3104.7104.781401013449-0.0814010134490508
4102.899.87270272486652.92729727513349
598.197.58474000060310.515259999396888
6113.9103.77590894702910.1240910529708
780.994.8285041181774-13.9285041181774
895.790.25417279104015.44582720895987
9113.2105.0775090615478.12249093845346
10105.999.93885876253035.96114123746972
11108.8105.2676080372433.53239196275694
12102.3101.428166670660.87183332934006
139999.9420470053093-0.942047005309262
14100.7101.358025329522-0.658025329522445
15115.5113.3139357506862.18606424931362
16100.798.7659839502141.9340160497859
17109.9107.1351212450292.76487875497115
18114.6108.4335331167676.16646688323274
1985.4100.188338759985-14.7883387599852
20100.594.57384322620675.92615677379334
21114.8107.8138184266046.9861815733964
22116.5108.7404014842447.7595985157562
23112.9108.9791211623203.9208788376803
2410299.21512765170252.78487234829750
25106104.8423761565971.15762384340301
26105.3103.8010163589141.49898364108639
27118.8113.6160217539945.18397824600555
28106.1103.8663753358832.23362466411736
29109.3108.4937111992200.806288800779543
30117.2112.3064510325304.89354896747037
3192.5107.707809354203-15.2078093542026
32104.299.9181351844674.28186481553307
33112.5108.612074712393.88792528761002
34122.4117.1438123889335.25618761106745
35113.3110.9347095768752.36529042312524
36100101.028440732246-1.02844073224569
37110.7110.0962017260040.603798273996215
38112.8111.1431409485501.65685905144962
39109.8111.9469766592-2.14697665919997
40117.3117.2733347518290.0266652481714963
41109.1112.403692437288-3.30369243728843
42115.9117.503685292610-1.60368529260959
4396115.317347806926-19.3173478069261
4499.899.71289205557030.0871079444297108
45116.8116.5244962291160.275503770883736
46115.7113.941222517451.75877748254990
4799.497.70908146898321.69091853101679
4894.392.71111238258931.58888761741068
499190.60328537533810.396714624661882
5093.292.40743225789180.792567742108253
51103.196.32219586012826.7778041398718
5294.192.86773480910661.23226519089343
5391.890.30080084168271.49919915831732
54102.797.89918044467974.80081955532029
5582.694.1031788859602-11.5031788859602
5689.185.29047731452023.80952268547976
57104.599.9041866223094.59581337769109
58105.199.3849015796835.71509842031697
5995.199.1776657990495-4.07766579904952
6088.798.3794095132631-9.67940951326313
6186.395.8017152264602-9.5017152264602
6291.897.9222952048273-6.12229520482731
63111.5110.7422194190940.75778058090598
6499.7103.014715983548-3.3147159835484
6597.5103.124710359423-5.6247103594231
66111.7114.528257719129-2.82825771912926
6786.2105.849462344406-19.6494623444064
6895.499.1768687383548-3.77686873835476







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.01223938633299490.02447877266598990.987760613667005
60.1652436598981720.3304873197963450.834756340101828
70.6469196208779930.7061607582440140.353080379122007
80.7339696766350540.5320606467298920.266030323364946
90.6802063599472780.6395872801054440.319793640052722
100.6117557467184240.7764885065631530.388244253281576
110.5180247128664990.9639505742670020.481975287133501
120.4273135666430620.8546271332861240.572686433356938
130.3532615401369580.7065230802739150.646738459863042
140.2875059834570420.5750119669140850.712494016542958
150.2408480416327380.4816960832654760.759151958367262
160.1771419858250560.3542839716501130.822858014174944
170.1274992270228080.2549984540456160.872500772977192
180.09965527496326390.1993105499265280.900344725036736
190.4734004430575070.9468008861150140.526599556942493
200.4746051841619830.9492103683239670.525394815838017
210.4476959447464430.8953918894928860.552304055253557
220.4339155204708490.8678310409416980.566084479529151
230.3745889634559350.7491779269118690.625411036544065
240.3148800140732180.6297600281464370.685119985926782
250.257386992089780.514773984179560.74261300791022
260.2049030206324740.4098060412649490.795096979367526
270.1757865710480420.3515731420960850.824213428951958
280.1368354937564810.2736709875129620.86316450624352
290.1085773844945260.2171547689890520.891422615505474
300.09199417744950260.1839883548990050.908005822550497
310.4437661425241990.8875322850483980.556233857475801
320.4085753412137320.8171506824274630.591424658786268
330.3688063184861940.7376126369723870.631193681513806
340.3601830156651940.7203660313303870.639816984334806
350.3194079097989250.638815819597850.680592090201075
360.2633039151396580.5266078302793150.736696084860342
370.2221387833372890.4442775666745770.777861216662711
380.1908053288703430.3816106577406860.809194671129657
390.1637093444499960.3274186888999920.836290655550004
400.1438801966223210.2877603932446420.856119803377679
410.1232948385353060.2465896770706130.876705161464694
420.1065125786945490.2130251573890980.893487421305451
430.5012860190507330.9974279618985340.498713980949267
440.4311689815712350.862337963142470.568831018428765
450.3812732408690670.7625464817381350.618726759130933
460.3685462794257290.7370925588514570.631453720574271
470.3133007507936850.6266015015873710.686699249206315
480.2531688904104680.5063377808209370.746831109589532
490.1950910077693420.3901820155386850.804908992230658
500.1468672981244640.2937345962489290.853132701875536
510.1757112005258340.3514224010516680.824288799474166
520.1350257318590710.2700514637181430.864974268140929
530.1021064533556490.2042129067112970.897893546644351
540.1159406029981000.2318812059962010.8840593970019
550.1784642301189070.3569284602378140.821535769881093
560.1672578505493330.3345157010986650.832742149450667
570.2248991636139970.4497983272279940.775100836386003
580.4475054589585610.8950109179171220.552494541041439
590.3786324333847410.7572648667694820.621367566615259
600.315337436552940.630674873105880.68466256344706
610.2428258149541850.4856516299083710.757174185045815
620.1585038437578590.3170076875157180.841496156242141
630.1348880006463320.2697760012926650.865111999353668

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0122393863329949 & 0.0244787726659899 & 0.987760613667005 \tabularnewline
6 & 0.165243659898172 & 0.330487319796345 & 0.834756340101828 \tabularnewline
7 & 0.646919620877993 & 0.706160758244014 & 0.353080379122007 \tabularnewline
8 & 0.733969676635054 & 0.532060646729892 & 0.266030323364946 \tabularnewline
9 & 0.680206359947278 & 0.639587280105444 & 0.319793640052722 \tabularnewline
10 & 0.611755746718424 & 0.776488506563153 & 0.388244253281576 \tabularnewline
11 & 0.518024712866499 & 0.963950574267002 & 0.481975287133501 \tabularnewline
12 & 0.427313566643062 & 0.854627133286124 & 0.572686433356938 \tabularnewline
13 & 0.353261540136958 & 0.706523080273915 & 0.646738459863042 \tabularnewline
14 & 0.287505983457042 & 0.575011966914085 & 0.712494016542958 \tabularnewline
15 & 0.240848041632738 & 0.481696083265476 & 0.759151958367262 \tabularnewline
16 & 0.177141985825056 & 0.354283971650113 & 0.822858014174944 \tabularnewline
17 & 0.127499227022808 & 0.254998454045616 & 0.872500772977192 \tabularnewline
18 & 0.0996552749632639 & 0.199310549926528 & 0.900344725036736 \tabularnewline
19 & 0.473400443057507 & 0.946800886115014 & 0.526599556942493 \tabularnewline
20 & 0.474605184161983 & 0.949210368323967 & 0.525394815838017 \tabularnewline
21 & 0.447695944746443 & 0.895391889492886 & 0.552304055253557 \tabularnewline
22 & 0.433915520470849 & 0.867831040941698 & 0.566084479529151 \tabularnewline
23 & 0.374588963455935 & 0.749177926911869 & 0.625411036544065 \tabularnewline
24 & 0.314880014073218 & 0.629760028146437 & 0.685119985926782 \tabularnewline
25 & 0.25738699208978 & 0.51477398417956 & 0.74261300791022 \tabularnewline
26 & 0.204903020632474 & 0.409806041264949 & 0.795096979367526 \tabularnewline
27 & 0.175786571048042 & 0.351573142096085 & 0.824213428951958 \tabularnewline
28 & 0.136835493756481 & 0.273670987512962 & 0.86316450624352 \tabularnewline
29 & 0.108577384494526 & 0.217154768989052 & 0.891422615505474 \tabularnewline
30 & 0.0919941774495026 & 0.183988354899005 & 0.908005822550497 \tabularnewline
31 & 0.443766142524199 & 0.887532285048398 & 0.556233857475801 \tabularnewline
32 & 0.408575341213732 & 0.817150682427463 & 0.591424658786268 \tabularnewline
33 & 0.368806318486194 & 0.737612636972387 & 0.631193681513806 \tabularnewline
34 & 0.360183015665194 & 0.720366031330387 & 0.639816984334806 \tabularnewline
35 & 0.319407909798925 & 0.63881581959785 & 0.680592090201075 \tabularnewline
36 & 0.263303915139658 & 0.526607830279315 & 0.736696084860342 \tabularnewline
37 & 0.222138783337289 & 0.444277566674577 & 0.777861216662711 \tabularnewline
38 & 0.190805328870343 & 0.381610657740686 & 0.809194671129657 \tabularnewline
39 & 0.163709344449996 & 0.327418688899992 & 0.836290655550004 \tabularnewline
40 & 0.143880196622321 & 0.287760393244642 & 0.856119803377679 \tabularnewline
41 & 0.123294838535306 & 0.246589677070613 & 0.876705161464694 \tabularnewline
42 & 0.106512578694549 & 0.213025157389098 & 0.893487421305451 \tabularnewline
43 & 0.501286019050733 & 0.997427961898534 & 0.498713980949267 \tabularnewline
44 & 0.431168981571235 & 0.86233796314247 & 0.568831018428765 \tabularnewline
45 & 0.381273240869067 & 0.762546481738135 & 0.618726759130933 \tabularnewline
46 & 0.368546279425729 & 0.737092558851457 & 0.631453720574271 \tabularnewline
47 & 0.313300750793685 & 0.626601501587371 & 0.686699249206315 \tabularnewline
48 & 0.253168890410468 & 0.506337780820937 & 0.746831109589532 \tabularnewline
49 & 0.195091007769342 & 0.390182015538685 & 0.804908992230658 \tabularnewline
50 & 0.146867298124464 & 0.293734596248929 & 0.853132701875536 \tabularnewline
51 & 0.175711200525834 & 0.351422401051668 & 0.824288799474166 \tabularnewline
52 & 0.135025731859071 & 0.270051463718143 & 0.864974268140929 \tabularnewline
53 & 0.102106453355649 & 0.204212906711297 & 0.897893546644351 \tabularnewline
54 & 0.115940602998100 & 0.231881205996201 & 0.8840593970019 \tabularnewline
55 & 0.178464230118907 & 0.356928460237814 & 0.821535769881093 \tabularnewline
56 & 0.167257850549333 & 0.334515701098665 & 0.832742149450667 \tabularnewline
57 & 0.224899163613997 & 0.449798327227994 & 0.775100836386003 \tabularnewline
58 & 0.447505458958561 & 0.895010917917122 & 0.552494541041439 \tabularnewline
59 & 0.378632433384741 & 0.757264866769482 & 0.621367566615259 \tabularnewline
60 & 0.31533743655294 & 0.63067487310588 & 0.68466256344706 \tabularnewline
61 & 0.242825814954185 & 0.485651629908371 & 0.757174185045815 \tabularnewline
62 & 0.158503843757859 & 0.317007687515718 & 0.841496156242141 \tabularnewline
63 & 0.134888000646332 & 0.269776001292665 & 0.865111999353668 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99624&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.0122393863329949[/C][C]0.0244787726659899[/C][C]0.987760613667005[/C][/ROW]
[ROW][C]6[/C][C]0.165243659898172[/C][C]0.330487319796345[/C][C]0.834756340101828[/C][/ROW]
[ROW][C]7[/C][C]0.646919620877993[/C][C]0.706160758244014[/C][C]0.353080379122007[/C][/ROW]
[ROW][C]8[/C][C]0.733969676635054[/C][C]0.532060646729892[/C][C]0.266030323364946[/C][/ROW]
[ROW][C]9[/C][C]0.680206359947278[/C][C]0.639587280105444[/C][C]0.319793640052722[/C][/ROW]
[ROW][C]10[/C][C]0.611755746718424[/C][C]0.776488506563153[/C][C]0.388244253281576[/C][/ROW]
[ROW][C]11[/C][C]0.518024712866499[/C][C]0.963950574267002[/C][C]0.481975287133501[/C][/ROW]
[ROW][C]12[/C][C]0.427313566643062[/C][C]0.854627133286124[/C][C]0.572686433356938[/C][/ROW]
[ROW][C]13[/C][C]0.353261540136958[/C][C]0.706523080273915[/C][C]0.646738459863042[/C][/ROW]
[ROW][C]14[/C][C]0.287505983457042[/C][C]0.575011966914085[/C][C]0.712494016542958[/C][/ROW]
[ROW][C]15[/C][C]0.240848041632738[/C][C]0.481696083265476[/C][C]0.759151958367262[/C][/ROW]
[ROW][C]16[/C][C]0.177141985825056[/C][C]0.354283971650113[/C][C]0.822858014174944[/C][/ROW]
[ROW][C]17[/C][C]0.127499227022808[/C][C]0.254998454045616[/C][C]0.872500772977192[/C][/ROW]
[ROW][C]18[/C][C]0.0996552749632639[/C][C]0.199310549926528[/C][C]0.900344725036736[/C][/ROW]
[ROW][C]19[/C][C]0.473400443057507[/C][C]0.946800886115014[/C][C]0.526599556942493[/C][/ROW]
[ROW][C]20[/C][C]0.474605184161983[/C][C]0.949210368323967[/C][C]0.525394815838017[/C][/ROW]
[ROW][C]21[/C][C]0.447695944746443[/C][C]0.895391889492886[/C][C]0.552304055253557[/C][/ROW]
[ROW][C]22[/C][C]0.433915520470849[/C][C]0.867831040941698[/C][C]0.566084479529151[/C][/ROW]
[ROW][C]23[/C][C]0.374588963455935[/C][C]0.749177926911869[/C][C]0.625411036544065[/C][/ROW]
[ROW][C]24[/C][C]0.314880014073218[/C][C]0.629760028146437[/C][C]0.685119985926782[/C][/ROW]
[ROW][C]25[/C][C]0.25738699208978[/C][C]0.51477398417956[/C][C]0.74261300791022[/C][/ROW]
[ROW][C]26[/C][C]0.204903020632474[/C][C]0.409806041264949[/C][C]0.795096979367526[/C][/ROW]
[ROW][C]27[/C][C]0.175786571048042[/C][C]0.351573142096085[/C][C]0.824213428951958[/C][/ROW]
[ROW][C]28[/C][C]0.136835493756481[/C][C]0.273670987512962[/C][C]0.86316450624352[/C][/ROW]
[ROW][C]29[/C][C]0.108577384494526[/C][C]0.217154768989052[/C][C]0.891422615505474[/C][/ROW]
[ROW][C]30[/C][C]0.0919941774495026[/C][C]0.183988354899005[/C][C]0.908005822550497[/C][/ROW]
[ROW][C]31[/C][C]0.443766142524199[/C][C]0.887532285048398[/C][C]0.556233857475801[/C][/ROW]
[ROW][C]32[/C][C]0.408575341213732[/C][C]0.817150682427463[/C][C]0.591424658786268[/C][/ROW]
[ROW][C]33[/C][C]0.368806318486194[/C][C]0.737612636972387[/C][C]0.631193681513806[/C][/ROW]
[ROW][C]34[/C][C]0.360183015665194[/C][C]0.720366031330387[/C][C]0.639816984334806[/C][/ROW]
[ROW][C]35[/C][C]0.319407909798925[/C][C]0.63881581959785[/C][C]0.680592090201075[/C][/ROW]
[ROW][C]36[/C][C]0.263303915139658[/C][C]0.526607830279315[/C][C]0.736696084860342[/C][/ROW]
[ROW][C]37[/C][C]0.222138783337289[/C][C]0.444277566674577[/C][C]0.777861216662711[/C][/ROW]
[ROW][C]38[/C][C]0.190805328870343[/C][C]0.381610657740686[/C][C]0.809194671129657[/C][/ROW]
[ROW][C]39[/C][C]0.163709344449996[/C][C]0.327418688899992[/C][C]0.836290655550004[/C][/ROW]
[ROW][C]40[/C][C]0.143880196622321[/C][C]0.287760393244642[/C][C]0.856119803377679[/C][/ROW]
[ROW][C]41[/C][C]0.123294838535306[/C][C]0.246589677070613[/C][C]0.876705161464694[/C][/ROW]
[ROW][C]42[/C][C]0.106512578694549[/C][C]0.213025157389098[/C][C]0.893487421305451[/C][/ROW]
[ROW][C]43[/C][C]0.501286019050733[/C][C]0.997427961898534[/C][C]0.498713980949267[/C][/ROW]
[ROW][C]44[/C][C]0.431168981571235[/C][C]0.86233796314247[/C][C]0.568831018428765[/C][/ROW]
[ROW][C]45[/C][C]0.381273240869067[/C][C]0.762546481738135[/C][C]0.618726759130933[/C][/ROW]
[ROW][C]46[/C][C]0.368546279425729[/C][C]0.737092558851457[/C][C]0.631453720574271[/C][/ROW]
[ROW][C]47[/C][C]0.313300750793685[/C][C]0.626601501587371[/C][C]0.686699249206315[/C][/ROW]
[ROW][C]48[/C][C]0.253168890410468[/C][C]0.506337780820937[/C][C]0.746831109589532[/C][/ROW]
[ROW][C]49[/C][C]0.195091007769342[/C][C]0.390182015538685[/C][C]0.804908992230658[/C][/ROW]
[ROW][C]50[/C][C]0.146867298124464[/C][C]0.293734596248929[/C][C]0.853132701875536[/C][/ROW]
[ROW][C]51[/C][C]0.175711200525834[/C][C]0.351422401051668[/C][C]0.824288799474166[/C][/ROW]
[ROW][C]52[/C][C]0.135025731859071[/C][C]0.270051463718143[/C][C]0.864974268140929[/C][/ROW]
[ROW][C]53[/C][C]0.102106453355649[/C][C]0.204212906711297[/C][C]0.897893546644351[/C][/ROW]
[ROW][C]54[/C][C]0.115940602998100[/C][C]0.231881205996201[/C][C]0.8840593970019[/C][/ROW]
[ROW][C]55[/C][C]0.178464230118907[/C][C]0.356928460237814[/C][C]0.821535769881093[/C][/ROW]
[ROW][C]56[/C][C]0.167257850549333[/C][C]0.334515701098665[/C][C]0.832742149450667[/C][/ROW]
[ROW][C]57[/C][C]0.224899163613997[/C][C]0.449798327227994[/C][C]0.775100836386003[/C][/ROW]
[ROW][C]58[/C][C]0.447505458958561[/C][C]0.895010917917122[/C][C]0.552494541041439[/C][/ROW]
[ROW][C]59[/C][C]0.378632433384741[/C][C]0.757264866769482[/C][C]0.621367566615259[/C][/ROW]
[ROW][C]60[/C][C]0.31533743655294[/C][C]0.63067487310588[/C][C]0.68466256344706[/C][/ROW]
[ROW][C]61[/C][C]0.242825814954185[/C][C]0.485651629908371[/C][C]0.757174185045815[/C][/ROW]
[ROW][C]62[/C][C]0.158503843757859[/C][C]0.317007687515718[/C][C]0.841496156242141[/C][/ROW]
[ROW][C]63[/C][C]0.134888000646332[/C][C]0.269776001292665[/C][C]0.865111999353668[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99624&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99624&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.01223938633299490.02447877266598990.987760613667005
60.1652436598981720.3304873197963450.834756340101828
70.6469196208779930.7061607582440140.353080379122007
80.7339696766350540.5320606467298920.266030323364946
90.6802063599472780.6395872801054440.319793640052722
100.6117557467184240.7764885065631530.388244253281576
110.5180247128664990.9639505742670020.481975287133501
120.4273135666430620.8546271332861240.572686433356938
130.3532615401369580.7065230802739150.646738459863042
140.2875059834570420.5750119669140850.712494016542958
150.2408480416327380.4816960832654760.759151958367262
160.1771419858250560.3542839716501130.822858014174944
170.1274992270228080.2549984540456160.872500772977192
180.09965527496326390.1993105499265280.900344725036736
190.4734004430575070.9468008861150140.526599556942493
200.4746051841619830.9492103683239670.525394815838017
210.4476959447464430.8953918894928860.552304055253557
220.4339155204708490.8678310409416980.566084479529151
230.3745889634559350.7491779269118690.625411036544065
240.3148800140732180.6297600281464370.685119985926782
250.257386992089780.514773984179560.74261300791022
260.2049030206324740.4098060412649490.795096979367526
270.1757865710480420.3515731420960850.824213428951958
280.1368354937564810.2736709875129620.86316450624352
290.1085773844945260.2171547689890520.891422615505474
300.09199417744950260.1839883548990050.908005822550497
310.4437661425241990.8875322850483980.556233857475801
320.4085753412137320.8171506824274630.591424658786268
330.3688063184861940.7376126369723870.631193681513806
340.3601830156651940.7203660313303870.639816984334806
350.3194079097989250.638815819597850.680592090201075
360.2633039151396580.5266078302793150.736696084860342
370.2221387833372890.4442775666745770.777861216662711
380.1908053288703430.3816106577406860.809194671129657
390.1637093444499960.3274186888999920.836290655550004
400.1438801966223210.2877603932446420.856119803377679
410.1232948385353060.2465896770706130.876705161464694
420.1065125786945490.2130251573890980.893487421305451
430.5012860190507330.9974279618985340.498713980949267
440.4311689815712350.862337963142470.568831018428765
450.3812732408690670.7625464817381350.618726759130933
460.3685462794257290.7370925588514570.631453720574271
470.3133007507936850.6266015015873710.686699249206315
480.2531688904104680.5063377808209370.746831109589532
490.1950910077693420.3901820155386850.804908992230658
500.1468672981244640.2937345962489290.853132701875536
510.1757112005258340.3514224010516680.824288799474166
520.1350257318590710.2700514637181430.864974268140929
530.1021064533556490.2042129067112970.897893546644351
540.1159406029981000.2318812059962010.8840593970019
550.1784642301189070.3569284602378140.821535769881093
560.1672578505493330.3345157010986650.832742149450667
570.2248991636139970.4497983272279940.775100836386003
580.4475054589585610.8950109179171220.552494541041439
590.3786324333847410.7572648667694820.621367566615259
600.315337436552940.630674873105880.68466256344706
610.2428258149541850.4856516299083710.757174185045815
620.1585038437578590.3170076875157180.841496156242141
630.1348880006463320.2697760012926650.865111999353668







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0169491525423729OK
10% type I error level10.0169491525423729OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0169491525423729 & OK \tabularnewline
10% type I error level & 1 & 0.0169491525423729 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99624&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0169491525423729[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0169491525423729[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99624&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99624&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0169491525423729OK
10% type I error level10.0169491525423729OK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}